An STDP-Based Supervised Learning Algorithm for Spiking Neural Networks
Compared with rate-based artificial neural networks, Spiking Neural Networks (SNN) provide a more biological plausible model for the brain. But how they perform supervised learning remains elusive. Inspired by recent works of Bengio et al., we propose a supervised learning algorithm based on Spike-Timing Dependent Plasticity (STDP) for a hierarchical SNN consisting of Leaky Integrate-and-fire (LIF) neurons. A time window is designed for the presynaptic neuron and only the spikes in this window take part in the STDP updating process. The model is trained on the MNIST dataset. The classification accuracy approach that of a Multilayer Perceptron (MLP) with similar architecture trained by the standard back-propagation algorithm.
KeywordsSTDP SNN Supervised learning
This work was supported in part by the National Natural Science Foundation of China under Grant 91420201, Grant 61332007, Grant 61621136008 and Grant 61620106010, in part by the Beijing Municipal Science and Technology Commission under Grant Z161100000216126, and in part by Huawei Technology under Contract YB2015120018.
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